{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:06:20.705374600Z",
     "start_time": "2024-07-19T03:06:16.769504700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=12, micro=3, releaselevel='final', serial=0)\n",
      "matplotlib 3.9.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.2\n",
      "sklearn 1.5.0\n",
      "torch 2.3.1+cpu\n",
      "cpu\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:06:30.871102800Z",
     "start_time": "2024-07-19T03:06:30.755031400Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 20640\n",
      "\n",
      ":Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      ":Attribute Information:\n",
      "    - MedInc        median income in block group\n",
      "    - HouseAge      median house age in block group\n",
      "    - AveRooms      average number of rooms per household\n",
      "    - AveBedrms     average number of bedrooms per household\n",
      "    - Population    block group population\n",
      "    - AveOccup      average number of household members\n",
      "    - Latitude      block group latitude\n",
      "    - Longitude     block group longitude\n",
      "\n",
      ":Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
      "\n",
      "The target variable is the median house value for California districts,\n",
      "expressed in hundreds of thousands of dollars ($100,000).\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "A household is a group of people residing within a home. Since the average\n",
      "number of rooms and bedrooms in this dataset are provided per household, these\n",
      "columns may take surprisingly large values for block groups with few households\n",
      "and many empty houses, such as vacation resorts.\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n",
      "(20640, 8)\n",
      "(20640,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "print(housing.DESCR)\n",
    "print(housing.data.shape)\n",
    "print(housing.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-25T03:08:06.127468600Z",
     "start_time": "2024-04-25T03:08:06.117474500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[ 8.32520000e+00,  4.10000000e+01,  6.98412698e+00,\n",
      "         1.02380952e+00,  3.22000000e+02,  2.55555556e+00,\n",
      "         3.78800000e+01, -1.22230000e+02],\n",
      "       [ 8.30140000e+00,  2.10000000e+01,  6.23813708e+00,\n",
      "         9.71880492e-01,  2.40100000e+03,  2.10984183e+00,\n",
      "         3.78600000e+01, -1.22220000e+02],\n",
      "       [ 7.25740000e+00,  5.20000000e+01,  8.28813559e+00,\n",
      "         1.07344633e+00,  4.96000000e+02,  2.80225989e+00,\n",
      "         3.78500000e+01, -1.22240000e+02],\n",
      "       [ 5.64310000e+00,  5.20000000e+01,  5.81735160e+00,\n",
      "         1.07305936e+00,  5.58000000e+02,  2.54794521e+00,\n",
      "         3.78500000e+01, -1.22250000e+02],\n",
      "       [ 3.84620000e+00,  5.20000000e+01,  6.28185328e+00,\n",
      "         1.08108108e+00,  5.65000000e+02,  2.18146718e+00,\n",
      "         3.78500000e+01, -1.22250000e+02]])\n",
      "--------------------------------------------------\n",
      "array([4.526, 3.585, 3.521, 3.413, 3.422])\n"
     ]
    }
   ],
   "source": [
    "# print(housing.data[0:5])\n",
    "import pprint  #打印的格式比较 好看\n",
    "\n",
    "pprint.pprint(housing.data[0:5])\n",
    "print('-'*50)\n",
    "pprint.pprint(housing.target[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:06:35.268648700Z",
     "start_time": "2024-07-19T03:06:35.196305200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11610, 8) (11610,)\n",
      "(3870, 8) (3870,)\n",
      "(5160, 8) (5160,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#拆分训练集和测试集，random_state是随机种子,同样的随机数种子，是为了得到同样的随机值\n",
    "x_train_all, x_test, y_train_all, y_test = train_test_split(\n",
    "    housing.data, housing.target, random_state = 7)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(\n",
    "    x_train_all, y_train_all, random_state = 11)\n",
    "# 训练集\n",
    "print(x_train.shape, y_train.shape)\n",
    "# 验证集\n",
    "print(x_valid.shape, y_valid.shape)\n",
    "# 测试集\n",
    "print(x_test.shape, y_test.shape)\n",
    "\n",
    "dataset_maps = {\n",
    "    \"train\": [x_train, y_train],\n",
    "    \"valid\": [x_valid, y_valid],\n",
    "    \"test\": [x_test, y_test],\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:06:38.088697600Z",
     "start_time": "2024-07-19T03:06:38.076607700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "StandardScaler()",
      "text/html": "<style>#sk-container-id-1 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-1 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n  padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(x_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:06:47.782145300Z",
     "start_time": "2024-07-19T03:06:47.772509100Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "\n",
    "class HousingDataset(Dataset):\n",
    "    def __init__(self, mode='train'):\n",
    "        self.x, self.y = dataset_maps[mode]\n",
    "        self.x = torch.from_numpy(scaler.transform(self.x)).float()\n",
    "        self.y = torch.from_numpy(self.y).float().reshape(-1, 1)\n",
    "            \n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        return self.x[idx], self.y[idx]\n",
    "    \n",
    "    \n",
    "train_ds = HousingDataset(\"train\")\n",
    "valid_ds = HousingDataset(\"valid\")\n",
    "test_ds = HousingDataset(\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.2981,  0.3523, -0.1092, -0.2506, -0.0341, -0.0060,  1.0806, -1.0611]),\n",
       " tensor([1.5140]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds[1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:06:51.609346100Z",
     "start_time": "2024-07-19T03:06:51.602620800Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "batch_size = 8\n",
    "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=False)\n",
    "val_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=False)\n",
    "test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型\n",
    "\n",
    "多输出模型常见于多任务学习（Multi-Task Learning）中，一个任务会有一个输出层，计算一个loss，最后多个任务的loss联合训练模型。除此之外，如果想拿到模型的中间输出，也可以使用多输出。\n",
    "\n",
    "例如，这里想拿到 deep_output，我们构建多输出模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:07:41.054920300Z",
     "start_time": "2024-07-19T03:07:41.045912400Z"
    }
   },
   "outputs": [],
   "source": [
    "#回归模型我们只需要1个数\n",
    "\n",
    "class WideDeep(nn.Module):\n",
    "    def __init__(self, input_dim=8):\n",
    "        super().__init__()\n",
    "        self.deep = nn.Sequential(\n",
    "            nn.Linear(input_dim, 30),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(30, 30),\n",
    "            nn.ReLU()\n",
    "            )\n",
    "        # pytorch 需要自行计算输出输出维度\n",
    "        self.output_layer = nn.Linear(30 + input_dim, 1)\n",
    "        \n",
    "        # 初始化权重\n",
    "        self.init_weights()\n",
    "        \n",
    "    def init_weights(self):\n",
    "        \"\"\"使用 xavier 均匀分布来初始化全连接层的权重 W\"\"\"\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Linear):\n",
    "                nn.init.xavier_uniform_(m.weight)\n",
    "                nn.init.zeros_(m.bias)\n",
    "        \n",
    "    def forward(self, x, return_deep_output=False):\n",
    "        # x.shape [batch size, 8]\n",
    "        deep_output = self.deep(x)\n",
    "        # concat [batch size, 30] with [batch size 8]\n",
    "        concat = torch.cat([x, deep_output], dim=-1)\n",
    "        logits = self.output_layer(concat)\n",
    "        # logits.shape [batch size, 1]\n",
    "        return (logits, deep_output) if return_deep_output else logits #如果return_deep_output为True，返回logits和deep_output，否则只返回logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:07:53.073135400Z",
     "start_time": "2024-07-19T03:07:53.070627100Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:07:55.103597500Z",
     "start_time": "2024-07-19T03:07:55.101084Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item())\n",
    "        \n",
    "    return np.mean(loss_list)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-19T03:11:31.628308900Z",
     "start_time": "2024-07-19T03:10:54.276529Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/14520 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d887bf0035234c1386f3200d5b29bf86"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits, deep_output = model(datas,return_deep_output=True)\n",
    "                deep_output= deep_output.mean(axis=1).reshape(-1, 1) # # 平均池化,尺寸为[batch size, 30]，求平均就变为[batch size],reshape成[batch size, 1]\n",
    "                logits=logits+deep_output  # 尺寸一致，相加，求损失\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    " \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                \n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "\n",
    "                    # 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict\n",
    "        \n",
    "\n",
    "epoch = 10\n",
    "\n",
    "model = WideDeep()\n",
    "\n",
    "# 1. 定义损失函数 采用MSE损失\n",
    "loss_fct = nn.MSELoss()\n",
    "# 2. 定义优化器 采用SGD\n",
    "# Optimizers specified in the torch.optim package\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.0)\n",
    "\n",
    "# 3. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=10, min_delta=1e-3)\n",
    "\n",
    "model = model.to(device)\n",
    "record = training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_loader)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HjsQ999yDyZMnY/z48Rg3bhzuvPNOREREAADmzZuHhx56CN999x3GjRuH6dOnS8HGVXAOix0cEiIicgGCYBmWccaXyrN5x1tfCBwwZcoUiKKI9evXIysrC7t27cLMmTMBAE8//TTWrl2L1157Dbt27UJCQgJiY2Oh1+tb6l6zsXTpUuzZswfDhw/H6tWr0a1bN+zfvx8AsHDhQpw6dQo333wztm7dil69emHt2rWt0i5HMbDYoeDW/ERE5CB3d3fcfvvtWLFiBX744Qd0794dAwcOBADs2bMHs2fPxrRp0xAbG4vw8HCkp6c75XZ79uyJ48ePo7KyUrpsz549UCgU6N69u3TZgAEDsGDBAuzduxd9+vTBypUrpeu6deuGf/7zn9i8eTNuv/12LFu2zCltcxYGFjusuZobxxERkSNmzpyJ9evX4+uvv5Z6VwDLvJA1a9YgISEBx48fxz333NNgRdHV3Ka7uztmzZqFxMREbNu2DY8//jjuvfdehIWFIS0tDYsWLcK+ffuQkZGBzZs3Izk5GT179kR1dTXmzp2L7du3IyMjA3v27MGhQ4ds5ri4As5hscfxnkAiIiLceOONCAwMRFJSEu655x7p8nfffRcPPPAAhg8fjuDgYDz77LPQarVOuU1PT09s2rQJTz75JIYMGQJPT0/ccccdePfdd6Xrk5OTMX36dBQVFSEiIgJz5szBP/7xDxiNRhQVFeG+++5DXl4egoODcfvtt2PRokVOaZuzMLDYwZ1uiYioORQKBS5ebDhBuEOHDti6davNZXPmzLH5vjlDRJeuXo2NjW1wfquwsDB8//338PX1bTDZWK1W44cffnD4duXCISE7rGvURU67JSIikg0Dix0KaR8WedtBRERtx4oVK+Dt7d3oV+/eveVuniw4JGQHJ90SEVFru/XWWzFs2LBGr3O1HWhbCwOLPdKQEBERUevw8fGBj4+P3M1wKRwSskNaJMTEQkTUqpy15Jfk54zHkj0sdnCnWyKi1qVWq6WVNiEhIVCr1dICiKtlNpuh1+tRU1Pj+Nb81wC56hZFEXq9HgUFBVAoFFCr1Vd8LgYWO+p2umVkISJqDQqFAh07dkROTk6jy4OvhiiKqK6uhoeHh9NC0F+B3HV7enoiOjr6qsISA4sddZNuZW0GEVGbolarER0dDaPRCJPJ5LTzGgwG7Ny5E6NGjWpTk1flrFupVMLNze2qgxIDix11Q0JMLERErUkQBKhUKqe+wCqVShiNRri7u7epwHIt1N12BvCuGD/8kIiISG4MLHa0oSFOIiIil8XAYgd3uiUiIpIfA4sdQu2QEHe6JSIikk+zA8vOnTsxZcoUREZGQhAErFu3zub62bNnQxAEm69JkybZPe/SpUvRoUMHuLu7Y9iwYTh48GBzm9YiuA8LERGR/JodWCorK9GvXz8sXbq0yWMmTZqEnJwc6cvex1avXr0a8+bNw0svvYSjR4+iX79+mDhxIvLz85vbPKezTmFhBwsREZF8mr2sefLkyZg8efJlj9FoNAgPD3f4nO+++y4efvhh3H///QCAzz77DOvXr8fXX3+N5557rsHxOp0OOp1O+l6r1QKwrDM3GAwO365DahOL0Wh0/rldmLXWtlQz0HbrBtpu7aybdbcFrlp3c9rTIvuwbN++HaGhoQgICMCNN96IV155BUFBQY0eq9frceTIESxYsEC6TKFQYNy4cdi3b1+jP7NkyRIsWrSoweWbN2+Gp6enc4qoVVGuBCDgyJGjqDrf9rpZ4uPj5W6CLNpq3UDbrZ11ty2s2zVUVVU5fKzTA8ukSZNw++23o2PHjkhNTcW///1vTJ48Gfv27YNSqWxwfGFhIUwmE8LCwmwuDwsLw9mzZxu9jQULFmDevHnS91qtFlFRUZgwYQJ8fX2dWs/n6XuBygr0HzAAY3s53mv0V2cwGBAfH4/x48f/ZTcZuhJttW6g7dbOull3W+CqdVtHSBzh9MBy9913S/+PjY1F37590blzZ2zfvh1jx451ym1oNBpoNJoGlzt7R0QAUAiWaT5KN6VLPcitpSXu07+Ctlo30HZrZ91tC+t2Dc1pS4sva+7UqROCg4ORkpLS6PXBwcFQKpXIy8uzuTwvL69Z82BaisB9WIiIiGTX4oElOzsbRUVFiIiIaPR6tVqNQYMGYcuWLdJlZrMZW7ZsQVxcXEs3zy4uayYiIpJfswNLRUUFEhISkJCQAABIS0tDQkICMjMzUVFRgfnz52P//v1IT0/Hli1bcNttt6FLly6YOHGidI6xY8fi448/lr6fN28evvzyS3zzzTc4c+YMHnvsMVRWVkqrhuRk3ThO5Mc1ExERyabZc1gOHz6MMWPGSN9bJ7/OmjULn376KU6cOIFvvvkGpaWliIyMxIQJE7B48WKbOSepqakoLCyUvr/rrrtQUFCAF198Ebm5uejfvz82btzYYCKuHNjDQkREJL9mB5bRo0dDvMyEjk2bNtk9R3p6eoPL5s6di7lz5za3OS2OG8cRERHJj58lZIdQ28Uiso+FiIhINgwsdvDTmomIiOTHwOIgflozERGRfBhY7LAOCREREZF8GFjs4KRbIiIi+TGw2MFlzURERPJjYLFDYV0lxC4WIiIi2TCw2GEdEuJGt0RERPJhYLGHQ0JERESyY2CxQwA3YiEiIpIbA4sdCvawEBERyY6BxQ7rKiFuHEdERCQfBhY7rENCzCtERETyYWCxh0NCREREsmNgsYM73RIREcmPgcUOhfRZQkwsREREcmFgsaNu0q287SAiImrLGFjs4JAQERGR/BhY7BCsnyXEISEiIiLZMLA4iD0sRERE8mFgsYM73RIREcmPgcUOaUiIXSxERESyYWCxg5NuiYiI5MfAYofAISEiIiLZMbDYUfdZQowsREREcmFgsYMbxxEREcmPgcUOaWd+IiIikg0Dix0cEiIiIpIfA4sdnHRLREQkPwYWO6SN45hYiIiIZMPAYk9tF4uZiYWIiEg2DCx2cOM4IiIi+TGw2MFVQkRERPJjYLGDq4SIiIjkx8BiBz+tmYiISH4MLHZwp1siIiL5MbDYxSEhIiIiuTU7sOzcuRNTpkxBZGQkBEHAunXrpOsMBgOeffZZxMbGwsvLC5GRkbjvvvtw8eLFy55z4cKFEATB5qtHjx7NLqYlcOM4IiIi+TU7sFRWVqJfv35YunRpg+uqqqpw9OhRvPDCCzh69CjWrFmDpKQk3HrrrXbP27t3b+Tk5Ehfu3fvbm7TWoS0SIiJhYiISDZuzf2ByZMnY/LkyY1e5+fnh/j4eJvLPv74YwwdOhSZmZmIjo5uuiFubggPD29uc1qcoraLRWRiISIikk2zA0tzlZWVQRAE+Pv7X/a45ORkREZGwt3dHXFxcViyZEmTAUen00Gn00nfa7VaAJYhKYPB4LS2A4AomgEARqPJ6ed2ZdZa21LNQNutG2i7tbNu1t0WuGrdzWmPIF7FbFJBELB27VpMnTq10etramowYsQI9OjRAytWrGjyPH/88QcqKirQvXt35OTkYNGiRbhw4QISExPh4+PT4PiFCxdi0aJFDS5fuXIlPD09r7ScRv2cpsCuXAUmtDPj5mizU89NRETUllVVVeGee+5BWVkZfH19L3tsiwUWg8GAO+64A9nZ2di+fbvdhtRXWlqKmJgYvPvuu3jwwQcbXN9YD0tUVBQKCwubdTuOWPjbKaw4eAH/GBmDpyd2d+q5XZnBYEB8fDzGjx8PlUold3NaTVutG2i7tbNu1t0WuGrdWq0WwcHBDgWWFhkSMhgMuPPOO5GRkYGtW7c2O0T4+/ujW7duSElJafR6jUYDjUbT4HKVSuX0B0KpUAIAFAqFSz3IraUl7tO/grZaN9B2a2fdbQvrdg3NaYvT92GxhpXk5GT8+eefCAoKavY5KioqkJqaioiICGc3r9kU3DiOiIhIds0OLBUVFUhISEBCQgIAIC0tDQkJCcjMzITBYMDf/vY3HD58GCtWrIDJZEJubi5yc3Oh1+ulc4wdOxYff/yx9P3TTz+NHTt2ID09HXv37sW0adOgVCoxY8aMq6/wKglcJURERCS7Zg8JHT58GGPGjJG+nzdvHgBg1qxZWLhwIX799VcAQP/+/W1+btu2bRg9ejQAIDU1FYWFhdJ12dnZmDFjBoqKihASEoKRI0di//79CAkJaW7znM66Dws3uiUiIpJPswPL6NGjL7tNvSNzeNPT022+X7VqVXOb0Wq40y0REZH8+FlCdkhDQuxiISIikg0Dix0cEiIiIpIfA4sdHBIiIiKSHwOLHQI4JERERCQ3BhY72MNCREQkPwYWO6TAwsRCREQkGwYWOzgkREREJD8GFjs4JERERCQ/BhY7uKyZiIhIfgwsdtT1sDCxEBERyYWBxY66nW5lbggREVEbxsBih3VIyMzAQkREJBsGFjusPSycdktERCQfBhY7OOmWiIhIfgwsdnBZMxERkfwYWOxQ1CYWM7tYiIiIZMPA4iDmFSIiIvkwsNjBISEiIiL5MbDYIXDWLRERkewYWOxQcOM4IiIi2TGw2MGN44iIiOTHwGKHtDU/Z7EQERHJhoHFQRwSIiIikg8Dix1cJURERCQ/BhY7FEwsREREsmNgsaNu0i0TCxERkVwYWOxgBwsREZH8GFjsqNs3jpGFiIhILgws9kjLmomIiEguDCx2KKxDQkwsREREsmFgsUOAdWt+JhYiIiK5MLDYwUm3RERE8mNgsYMf1kxERCQ/BhY76npYmFiIiIjkwsBih/XDD/lpzURERPJhYLHDOiTEDhYiIiL5MLDYwSEhIiIi+TU7sOzcuRNTpkxBZGQkBEHAunXrbK4XRREvvvgiIiIi4OHhgXHjxiE5OdnueZcuXYoOHTrA3d0dw4YNw8GDB5vbtBZRt6xZ5oYQERG1Yc0OLJWVlejXrx+WLl3a6PVvvvkmPvzwQ3z22Wc4cOAAvLy8MHHiRNTU1DR5ztWrV2PevHl46aWXcPToUfTr1w8TJ05Efn5+c5vndAouayYiIpJdswPL5MmT8corr2DatGkNrhNFEe+//z6ef/553Hbbbejbty++/fZbXLx4sUFPTH3vvvsuHn74Ydx///3o1asXPvvsM3h6euLrr79ubvOcT5p0y8hCREQkFzdnniwtLQ25ubkYN26cdJmfnx+GDRuGffv24e67727wM3q9HkeOHMGCBQukyxQKBcaNG4d9+/Y1ejs6nQ46nU76XqvVAgAMBgMMBoOzygEAmE0my79m0enndmXWWttSzUDbrRtou7WzbtbdFrhq3c1pj1MDS25uLgAgLCzM5vKwsDDpuksVFhbCZDI1+jNnz55t9GeWLFmCRYsWNbh88+bN8PT0vJKmNymxUACgRFFRETZs2ODUc/8VxMfHy90EWbTVuoG2WzvrbltYt2uoqqpy+FinBpbWsmDBAsybN0/6XqvVIioqChMmTICvr69Tb8twLBtIPo3AwEDcdNNQp57blRkMBsTHx2P8+PFQqVRyN6fVtNW6gbZbO+tm3W2Bq9ZtHSFxhFMDS3h4OAAgLy8PERER0uV5eXno379/oz8THBwMpVKJvLw8m8vz8vKk811Ko9FAo9E0uFylUjn9gVC51d5FguBSD3JraYn79K+grdYNtN3aWXfbwrpdQ3Pa4tR9WDp27Ijw8HBs2bJFukyr1eLAgQOIi4tr9GfUajUGDRpk8zNmsxlbtmxp8mdak3UfFu50S0REJJ9m97BUVFQgJSVF+j4tLQ0JCQkIDAxEdHQ0nnrqKbzyyivo2rUrOnbsiBdeeAGRkZGYOnWq9DNjx47FtGnTMHfuXADAvHnzMGvWLAwePBhDhw7F+++/j8rKStx///1XX6GTiFwlREREJJtmB5bDhw9jzJgx0vfWuSSzZs3C8uXL8cwzz6CyshKPPPIISktLMXLkSGzcuBHu7u7Sz6SmpqKwsFD6/q677kJBQQFefPFF5Obmon///ti4cWODibhysH6WEBEREcmn2YFl9OjRl+1tEAQBL7/8Ml5++eUmj0lPT29w2dy5c6UeF1dijSvsYCEiIpIPP0vIDkVtDwvzChERkXwYWOyom3TLyEJERCQXBhY7OCREREQkPwYWe6QPP2RiISIikgsDix3SKiHmFSIiItkwsNih4MZxREREsmNgsUOaw8IuFiIiItkwsNhhHRLipFsiIiL5MLDYwVVCRERE8mNgsUPgxnFERESyY2CxQ1okxC4WIiIi2TCw2MEhISIiIvkxsNghcOM4IiIi2TGw2CGAq4SIiIjkxsBiBze6JSIikh8Dix2cdEtERCQ/BhY7OCREREQkPwYWOzgkREREJD8GFju4rJmIiEh+DCx2KKSdbplYiIiI5MLAYk9tF4uZeYWIiEg2DCx2WIeEOCZEREQkHwYWO/jhh0RERPJjYLGDk26JiIjkx8Bih3XSrZmJhYiISDYMLHZwHxYiIiL5MbA4iomFiIhINgwsdrCHhYiISH4MLJcjilCaDfBGFUSzWe7WEBERtVkMLJdjqEaf5d2R6P4Q1KJO7tYQERG1WQwsl+Omkf6rhl7GhhAREbVtDCyXo1BCVKgAACqRgYWIiEguDCx2mJWWXhZ39rAQERHJhoHFDrE2sKhEg8wtISIiarsYWOyQAgt7WIiIiGTDwGKHWDvxVg32sBAREcmFgcUOaw+LmpNuiYiIZMPAYofo5g6Ay5qJiIjk5PTA0qFDBwiC0OBrzpw5jR6/fPnyBse6u7s7u1lXzNrDouGkWyIiItm4OfuEhw4dgslkkr5PTEzE+PHjMX369CZ/xtfXF0lJSdL3gvUDfFyAdQ4LJ90SERHJx+mBJSQkxOb7119/HZ07d8YNN9zQ5M8IgoDw8HCHb0On00Gnq9sqX6vVAgAMBgMMBuf2hJgVagCARtQ7/dyuzFprW6oZaLt1A223dtbNutsCV627Oe1xemCpT6/X4/vvv8e8efMu22tSUVGBmJgYmM1mDBw4EK+99hp69+7d5PFLlizBokWLGly+efNmeHp6OqXtVn1LtPAGoDLrsGHDBqee+68gPj5e7ibIoq3WDbTd2ll328K6XUNVVZXDxwqiKIot1ZAff/wR99xzDzIzMxEZGdnoMfv27UNycjL69u2LsrIyvP3229i5cydOnTqF9u3bN/ozjfWwREVFobCwEL6+vk6tofqnR+B7bg3eMM3EvBc/cOq5XZnBYEB8fDzGjx8PlUold3NaTVutG2i7tbNu1t0WuGrdWq0WwcHBKCsrs/v63aI9LF999RUmT57cZFgBgLi4OMTFxUnfDx8+HD179sTnn3+OxYsXN/ozGo0GGo2mweUqlcrpD0SNysNym9C71IPcWlriPv0raKt1A223dtbdtrBu19CctrRYYMnIyMCff/6JNWvWNOvnVCoVBgwYgJSUlBZqWTNx4zgiIiLZtdg+LMuWLUNoaChuvvnmZv2cyWTCyZMnERER0UItaybrxnFcJURERCSbFgksZrMZy5Ytw6xZs+DmZtuJc99992HBggXS9y+//DI2b96M8+fP4+jRo/j73/+OjIwMPPTQQy3RtGYT3axDQuxhISIikkuLDAn9+eefyMzMxAMPPNDguszMTCgUdTmppKQEDz/8MHJzcxEQEIBBgwZh79696NWrV0s0rfk4JERERCS7FgksEyZMQFOLj7Zv327z/XvvvYf33nuvJZrhHLWBRcMhISIiItnws4TssH6WkDsMTYYwIiIialkMLPYo63pYmFeIiIjkwcBij8oaWAxgXiEiIpIHA4sdQu2QkEbgkBAREZFcGFjsUdb1sJiZV4iIiGTBwGKPqraHBQaIHBQiIiKSBQOLHXUbx3HSLRERkVwYWOwQrPuwCNw4joiISC4MLHYItauE3NnDQkREJBsGFnuUdXNYzEwsREREsmBgsUPqYeGyZiIiItkwsNhTuw8LAIhGnYwNISIiarsYWOwQGFiIiIhkx8Bih+CmhlkULN8YauRtDBERURvFwGKHIAjQQQUAEI0MLERERHJgYLGjfmABh4SIiIhkwcBihwDUBRZDtaxtISIiaqsYWOwQBEAnWntYOCREREQkBwYWOyxDQmoAXCVEREQkFwYWB9TNYWEPCxERkRwYWBzAwEJERCQvBhYH6ETLkBBXCREREcmDgcUB1h4WgYGFiIhIFgwsDuDGcURERPJiYHEA57AQERHJi4HFAXppSIiBhYiISA4MLA7g1vxERETyYmBxgNTDYmJgISIikgMDiwOsO91yDgsREZE8GFgcoOeyZiIiIlkxsDigroeFgYWIiEgODCwOkDaOM3FIiIiISA4MLA7Qi1wlREREJCcGFgfoBUtgUXDSLRERkSwYWBzAZc1ERETyYmBxAOewEBERyYuBxQH62lVCXNZMREQkD6cHloULF0IQBJuvHj16XPZnfvrpJ/To0QPu7u6IjY3Fhg0bnN2sq2IdEgKHhIiIiGTRIj0svXv3Rk5OjvS1e/fuJo/du3cvZsyYgQcffBDHjh3D1KlTMXXqVCQmJrZE066IdR8WBXtYiIiIZNEigcXNzQ3h4eHSV3BwcJPHfvDBB5g0aRLmz5+Pnj17YvHixRg4cCA+/vjjlmjaFeGkWyIiInm5tcRJk5OTERkZCXd3d8TFxWHJkiWIjo5u9Nh9+/Zh3rx5NpdNnDgR69ata/L8Op0OOl1deNBqtQAAg8EAg8Fw9QXUYzAYbAKLs8/vqqx1tpV6rdpq3UDbrZ11s+62wFXrbk57nB5Yhg0bhuXLl6N79+7IycnBokWLcP311yMxMRE+Pj4Njs/NzUVYWJjNZWFhYcjNzW3yNpYsWYJFixY1uHzz5s3w9PS8+iIuYajdhwWGapebX9PS4uPj5W6CLNpq3UDbrZ11ty2s2zVUVVU5fKzTA8vkyZOl//ft2xfDhg1DTEwMfvzxRzz44INOuY0FCxbY9MpotVpERUVhwoQJ8PX1dcptWBkMBrx75BcAgBIm3DRpAqBokY4pl2IwGBAfH4/x48dDpVLJ3ZxW01brBtpu7aybdbcFrlq3dYTEES3+yuvv749u3bohJSWl0evDw8ORl5dnc1leXh7Cw8ObPKdGo4FGo2lwuUqlapEHQlolBEAlmAEXerBbWkvdp66urdYNtN3aWXfbwrpdQ3Pa0uL7sFRUVCA1NRURERGNXh8XF4ctW7bYXBYfH4+4uLiWbprDjEK9O5QrhYiIiFqd0wPL008/jR07diA9PR179+7FtGnToFQqMWPGDADAfffdhwULFkjHP/nkk9i4cSPeeecdnD17FgsXLsThw4cxd+5cZzftipmghEFUWr7h5wkRERG1OqcPCWVnZ2PGjBkoKipCSEgIRo4cif379yMkJAQAkJmZCYWiLicNHz4cK1euxPPPP49///vf6Nq1K9atW4c+ffo4u2lXTABQAzVUqGZgISIikoHTA8uqVasue/327dsbXDZ9+nRMnz7d2U1xGgGWzxPyQTWHhIiIiGTAzxJyhFD3AYjsYSEiImp9DCwOEADoROteLAwsRERErY2BxQHWISEA7GEhIiKSAQOLAwSbISHOYSEiImptDCwOsn5iM3tYiIiIWh8DiwNs5rCwh4WIiKjVMbA4iD0sRERE8mFgcQDnsBAREcmLgcUBXCVEREQkLwYWB3AOCxERkbwYWBxU18NSLW9DiIiI2iAGFgdY5rBYJ92yh4WIiKi1MbA4gHNYiIiI5MXA4qC6OSwMLERERK2NgcUBtj0sHBIiIiJqbQwsDhAEoIYbxxEREcmGgcVB7GEhIiKSDwOLA2z3YWEPCxERUWtjYHGAzdb8BgYWIiKi1sbA4iB++CEREZF8GFgcwFVCRERE8mJgcQDnsBAREcmLgcUBNnNY2MNCRETU6hhYHCJyHxYiIiIZMbA4wHZIiD0sRERErY2BxUE2q4REUd7GEBERtTEMLA6wmcMCETDpZW0PERFRW8PA4gCbZc0A57EQERG1MgYWBwgA9HCru4DzWIiIiFoVA4vDBJgUGst/2cNCRETUqhhYHCAIln9NSmtgYQ8LERFRa2JgcUBtXmEPCxERkUwYWJqhLrCwh4WIiKg1MbA4oG5IiLvdEhERyYGBxQENhoQMDCxEREStiYGlGUwK9rAQERHJgYHFAZx0S0REJC8GFgdIc1ikHhZOuiUiImpNTg8sS5YswZAhQ+Dj44PQ0FBMnToVSUlJl/2Z5cuXQxAEmy93d3dnN+2q1e3Dwh4WIiKi1uT0wLJjxw7MmTMH+/fvR3x8PAwGAyZMmIDKysrL/pyvry9ycnKkr4yMDGc37Yo1HBJiDwsREVFrcrN/SPNs3LjR5vvly5cjNDQUR44cwahRo5r8OUEQEB4e7tBt6HQ66HR1oUGr1QIADAYDDAbDFbS6aQaDQRoSMgiWD0A06atgdvLtuBrr/ejs+9PVtdW6gbZbO+tm3W2Bq9bdnPY4PbBcqqysDAAQGBh42eMqKioQExMDs9mMgQMH4rXXXkPv3r0bPXbJkiVYtGhRg8s3b94MT0/Pq2/0JYTajqiC0gp0A5B85iSSSjc4/XZcUXx8vNxNkEVbrRtou7Wz7raFdbuGqqoqh48VRFEUW6ohZrMZt956K0pLS7F79+4mj9u3bx+Sk5PRt29flJWV4e2338bOnTtx6tQptG/fvsHxjfWwREVFobCwEL6+vk6twWAwYObSLThWpMBvXdcjNmsFTHFPwHzji069HVdjMBgQHx+P8ePHQ6VSyd2cVtNW6wbabu2sm3W3Ba5at1arRXBwMMrKyuy+frdoD8ucOXOQmJh42bACAHFxcYiLi5O+Hz58OHr27InPP/8cixcvbnC8RqOBRqNpcLlKpWqRB8I6h8WstEwEVpr1ULrQA96SWuo+dXVttW6g7dbOutsW1u0amtOWFgssc+fOxe+//46dO3c22ktyOSqVCgMGDEBKSkoLte7KGLkPCxERkSycvkpIFEXMnTsXa9euxdatW9GxY8dmn8NkMuHkyZOIiIhwdvOuiHXSrZH7sBAREcnC6T0sc+bMwcqVK/HLL7/Ax8cHubm5AAA/Pz94eHgAAO677z60a9cOS5YsAQC8/PLLuO6669ClSxeUlpbirbfeQkZGBh566CFnN++KSMuaBW7NT0REJAenB5ZPP/0UADB69Giby5ctW4bZs2cDADIzM6FQ1HXulJSU4OGHH0Zubi4CAgIwaNAg7N27F7169XJ2866INbCwh4WIiEgeTg8sjiw62r59u8337733Ht577z1nN8V5pCEhzmEhIiKSAz9LyAF1PSzc6ZaIiEgODCwOkAIL57AQERHJgoGlGQzWOSwGBhYiIqLWxMDiAOuyZpOCPSxERERyYGBxQMMhIc5hISIiak0MLM1g4BwWIiIiWTCwOEBosKyZPSxEREStiYHFAdYhIT17WIiohSReKEPihTK5m0HkshhYmkGaw2I2AGaTvI0homuGzmjC3V/sx91f7IfOyOcWosYwsDjA2sNisA4JARwWIiKnKasyoEJnRIXOCG21Ue7mELkkBhYHSMuahXqfZMBhISJyEm1NXUip1DGwEDWGgcUB0qc1ww1Q1IYWBhYicpLyGoP0/woGFqJGMbA0gwgRcHO3fMPAQkROUs4eFiK7GFgcYO1hEUUAblzaTETOZRNY9AwsRI1hYHFEbWIRAfawEJHT2Q4JcZUQUWMYWBxg28NiDSzsYWltNQYT4k/noYrvQOkaU7+HpaKGv99EjWFgcUBdYOEcFjl9vz8DD397GF/sPC93U4icqn4PC+ewEDWOgcUBUmABOIdFRin5FQCAzKIqmVtC5Fz1lzVzlRBR4xhYHGGdw+JAD4vOaMKRjBIYTeZWalzzmc2ipZa/mPxyS0gsrTbYOZLor4WrhK6MtsaA2csO4ucj2XI3hVoBA4sDGl0lZGg8sHy6PRV3fLoXqw5ltUrb6tMbzdidXIhqfdOT9ip0Rox6axse+/5oK7bMOfLLLfd5SZVe5pYQOZfNkJCLzdESRRGvbTjjkqFgb0ohticV4KvdaXI3hVoBA4sDbIeELt/DcvqiFgBw6mLrf4jZ6sNZ+PtXB7B0W0qTx5zILkV2STW2ns3/y/Wy5GktPSxlVexhoWuLzaRbF1sldDa3HF/sPI+Xfzsld1MaKKq0vHkpqbz6NzH55TX48VAWagyudf9THQYWR9QmFrMo2p3DcrGsGgCQXVLd6PVZxVXYdCrX6U0EgNTaOR7J+eVNHpNWWAkA0JvMNuPmramgXId3NyfhQmnj91FjTGYRRRWW+5w9LHStKde57qRb61CstsbocvNrSmvfvBRX6a/6Ddh78efwzP9O4H9HXa8niSwYWBzQ+LLmxntYLtQGlQuNBJaLpdW4/s1t+Md3R3A0s8Tp7SysfUG3PsE05nxBpfR/awBobSsOZODDrSn4Ykeqwz9TVKGDufb5qKzaALP5r9U71Nb91XrzWlu5C0+6rf88kVvmWqsji2t7VvRGM6ouMxTuiNR8y3NjZjEn9bsqBhYHCPW/UTW9D0uV3oiS2sR/obTa5klaZzThsRV180aS85ruBblSUmDRXi6wVNQ7Xp6eCmuYS2vGap/6IcwsAuVOfFIvqzbgWAsESLLQG82Y/MEuPPTNYdnaIIoizuWVu+xkeFeedFtU73nC1QJL/d7W4qscFsrRWp6XCsvZg+uqGFiawd4qoYuldZfpjGYU1HtnsvlUHo5nlTZ6rLNYn1gKynVNvqM9X1jXw1IoUw+LNXxklzgeWPK0tvdXvrYGPxzMxMVmDCs15Zmfj2PaJ3uxL7Xoqs/VlPf/PIfpn+1tk5vepRZU4GxuOf48kydbr966hAuY8N5OfLAlWZbbvxxRFF36ww8LK+ses5yyq/97c6b6c1euZqjYbBalMCbX8yLZx8DiAMFma/7aOSw1pQ2Ou/TFs/6wUP2hGKDhH/6u5ALc8tEunMy+8sm61j80vcksje3WpzeakVWvu1OuFw9r+MguqW5yaEcURXy9Ow07zhUAaDjM9d3+DCxYcxJvbDx7VW3RGU3YnmS5jZYYprP6bl8GDqWX4GBacYvdhqvKrRc2E2snpbe2Q+mWx7YlH+MrpTOaYTDV/R24Wg9L/R6H5vawGExmpw8H5pRV44Hlh7DzXAGK6z3PXU0PS2GlTnoMCi4zpE7yYmBxgHVIyCyKQEBHyzeHvgJ2vl07scWiQWCp931WbW9Cp2Cv2mNt//B/OJiJxAta/Hr8whW10WgyS8NRQOPzWDKLK1E/HxS0wpCQKIp4e1MS1h6rm8hmfULQG81NvptJyivHy7+fxpOrjkEUxQbDXIdrX4BS6w1xXYnjWWXQGS3DBJeGSmepMZik1Qzphba3UVZtwMbEHJhcdE7Olb7YGExmPPvzCaw9lm3zIpd4ofVXzwF1E9Kbeox35ggY8eYOWdqnrbF9c+FqPSxF9XpYcrWOB5ayagNGvL4VD397pMF1VXqjTa9Sc6w7dhFbz+bj6z1pKK1yTg9LTr3n47bQw3Iyuwyzlx2UVrX+VTCwOMTypC2KAPrPRGnPmZbLti4GVv8d0Fnmo1waWOqvFLIOfwztGGg59pIelrO5lnNkNDGvw2wWoTc2Pf5+6buLS4dQACD1kifr1uhhOZ5dho+3pWDBmpPQG80wmMzSizdQF+QulV5ouby0yoCMoippDxarc3nW+7xhnfGn8xxeVl5/GOh84dWFn6bUf8FOuySwvPL7aTz6/dHLrkzIL6/B9M/2YuWBzBZpX1Pe3pSEIa/+2ayhO6v954uw+nAWXv/jrGsEltrf/ZyymkaH5Q4XKpBfrsMn25veEqCllF+yWq/GYHb6XJtjmSVY9Nupy+7R1JQrncNyNLME+eU67DiXbxPIRVHE9M/2Yczb2xuENUdYV0FeLK22ed4rrrzy7Q7q93gXVepdelJ/emEllm5Luapg+/3+DGxPKsAPB1v3OeVqMbA4oP4+LF/tv4D+x27GjxHzAaUaOPs78OVYoOAcLtS+eKrdLHdr/SGhrGLL/6XAUm9Sbo3BJL3zbmqG+jP/O4GBi+NthnTqu3QCbWM9LJe+WF76TqK0Su/0MWrrdvo1BjNOXSxrcJtNLf+u/yJ54kJZg3qMtU8oxZV6mxeg5LxyPPztYdy/7JBDTzr7z9cLLAWVLbKaJad+YKkXSEVRxPbaIa/LDQX+eCgLh9JLWv3FdP3JHBRW6LE3pflze6w9GnlaHdKL6n7vEltgf6JqvanRgG5VVmWw+b279O9AFEXk1/4abj6Vh/xm9CI4g/XDDoO9NdJllVe54qU+k1nEE6uOYdmedPyS0Pwe3PpvbHKaEVjO1b4JM5hEmzccp3O0OHVRi8IK/RUNgVt/t7KKq23C3tXsxVL/jY/JLLr0btpvbjqLtzYl4X9XsZGftWfa+vz8V8HA4gDrHJaDacV4bcMZAMCbBddBnL0e8IkACpOApUPw5LlZeMntGzwUfAa+qJSGhIwms9SVOqSDJbDUGOrmmaTkV0hDNRlFVQ1eNE1mERtO5qBCZ8SWM3mNtvHSIHBpjwRQt0Koe5gPANt3TqIo4u4v9mPCezsd6hLdmJiDzQ7sJ1N/yOZwekmDoZ2mAlj9IHMyu1R6EQn10TQ4tv6TzYnaJ8D8ch3O5Dbs7iwo1+Ef3x3G3tRC1BhMOFJvTkNZtcFmTNxZ6ofA+kNCaYWV0vDY5Ya2/jyTD8BynzT2uLYEk1mUQmP9wOGo+pO768/bySqutunGd4b5Px/HyDe2Sr1ul0q9pOfs0sBSXGVAtcnyR240i/jxcOvuUm190Q3yUkOltLSjsXksSbnlV/Si/OeZPOkNU3NfoERRtHkz1JwhoaR6j0f9v2frnDGgrme5Oe2x1lB9yQZvxc34vRJF4NRFrdTDc+kbNVeex5KQWQrg8vtt2WP9+0y5yiH11sbA4gBrD0tKfoXUtVlYocMF7z7Im7EJxo5jAADRxjTc77YJz5Quwgn3h/FexjRg6XUwLr8Vbys/xkvq79Hu5FI84rkDkxQHUXp6K5BzHNmppxEALVQwotpgavDHklpQIe0xcLT2l/VSDQJLI0ubk/Isv5zWXp78ch0eXH4ID397GPnlOpzNLUd5jVGaH9KUogod5qw8hke/P2J3lU5qvSfIQ+nFDd4JW59IL1X/Ce54dl0PS7fasFVf/blC9Z8kG+sZWHEgA5tO5eGtTUk4mlkCvdGMEB8N2vl7AGj4YuYM9d+VZpdUSUN7+8/XvZCnFlRgV3IBhr32J3aeq3tCzy+vQUK91WUJTTz+zpZTVi1NQmxqmPJy6s8VufRd+SknjpubzCK2ns2HwSRid3Jho8ekXvIifek8lkvnFf14uHU3DrPO5fBxd4OXxg1Aw8CSeKEMkz/YiSdXJ0iX/ZJwAX0XbsKB85fvAfu63rb1zZ3zVa4zQl9veKq4Uu/wTrBJufUDS93v0Pak/HrHNO93Iaespsnep2IH5+TpDCb8kKrA1E/3Y17t/Xnxkt9RR+exbDmTZ1NnSyuq0ElttQ6bN1dJlV4aSiso16HMhXuTLuUmdwP+Coxi3U4sPSN8YTKbcS6vAqsOZuGLnecxrNMCfPOvL/HUGx9jKE7i9sA0eGrPw08sBwrOwB3ANGXtCbZtwL8BQA1g/fsAgEkAJtWultaJblAs9QU8fACND6D2hm+1gC9VelRDDUWKB7C+E6DUWFYs1X5FpJfjXmURDHCDQXRD9MVTwKlkQKkClGoYoYR3zjEMFBSYEqzGESEdxhIl0kqUMECJbZF6hKAERiiRlJ6FsR01UJj1gMkAuLnVdTPB0qVrDW6/n7iIR0Z1bvK+q/9O+0hGCa7vFmJz/dm8cny6PRXDOwehX5S/dHn9EJJ4oW5ibNcwb+xOsX1hqh+a6j957E4pxMOjOtkceyTDEsaOZ5Xil2MXAQDXdwlGQYUOF0qrkVZYCa8mq3GMKIrILK5CO38PuCkVNu/ezKJl2K9LqDcOpNW90ORpdfhi53nkaXX4Zm86RtXeT1vP5Nuc+1hWKSb0Dr/KFlrsSy3CC78k4tWpfTCsUxAA4GC+gON/JOHGnnW3kVHceIj7/cRFVOlNuHNwVIPrzjfywtg11BvJ+RU4nl2KEV2CnVLD+Xph/kxO4y9+1vkrSoUAk1lsEErP1z7xD4jyw7GsMmQWV6GkUg+d0QwRIiL8PJzS1qZYe1h83N3gpXZDaZWhwfyEXcmFMIvAwbQimM0iFAoBPx3OhrbGiHUJF6XH71JJueU4UK+H63wzA7m1F9ZLrYTRLEJnNCNfq0N0kOdlf85kFpFcLyhm174xKasySH+D1vY1x+V6iBztYXn6f4k4UGB5r/7nmXyYzSJyLnnjZQ0sD31zCHlaHX78Rxw81EqbYxIvlOHBbw6jnb8Hdj87BoJgs2NXo7KKq3A8uxQ3x0Y4dPyl6of9K31zlXZJ0EktqMDA6AC7P6c3mqXpDnJhYHGAj6puiOabB4bgwy3JOJdXgc93psJgErEruRAJJd3wq3EofheGYuqciRi4aA2CxWJ8d2cHpGWkYcuhE+gXaMQtXTRISDoPQ0UBOnnp4WmuhKAvh7toSc0awQjoii1ftcIBhFv/VkwADm1t0MY4AHGqehfkAvip7ls3ACvcav/zJ7Dh0pGV3cDdtaEJhy1fUwDguPUAAVC4AQolhokKnNAAJiiAbW7AQXdAoQQEJaBQ1P6rhAgFlpZVwqRWwAwBZoMA3+3uWKM2QK1SodIgQswTYM4VUL5Vgawgb0QFeQOCAs+WFMKgEiw/JwowKwUoVEr0vOiP/qoyy+UQIEKBHod8gRx/QFBgWnYOxruZYYYCinQFTBs6QqmwtMsMBW7IzMAwNxFmCECCgKfcBExGO2Qba9BXWYaIkzsRbC7GmqU7oVGpcNuA9hAUSkBQ1H4Jln8h1LvM9mvTqVz8fjIPPh5q3NAjDBEFVRiv0EKsbXP5iXKI0YFAygmMVBhr6xBgSBUwTBBgOJ8EY5oObko3nD+WhAFCCcL8PHCxTIfS5EKgb3Xt7ddvi+3/VxzMwvFsLf45vpvlBdfaduuxEPDbzkRU5Bfgm01lGDZzCHKKK7H5fDlMqSdRqS2EHywvDiWFNRCrS6AzAo98dxjBPh546dY+eHbVARjNwKBId/zv2EWculiGT2YOgaAQkFtWZbmtetsu3tw3Au//mYw9KYX4v9FdmvhrsziZXYZdKQW4L64DvDVNP02drDeJt7EhQKCuV2Foh0DsO18khamvd6dhd0ohQrzVAIA+kb4orNQjq7gaJy6U4emfjsNgMmP706Ph76m+bHsdUVCuQ1ZJFQZGB0AULS/o+1KLsCvZ0qPm466Saq285POErMuxawxmZJVUISrAE8ezSwEApy8zL8ga7ntH+uLURS2yiqtQVm1AQlYprovxs9tm6/yVYB8NBADpRVXIKau2G1gyiiptFglYe0x3pRTALAK+7m7Q1hiRlFcOk1mEUuHYi/flAsulw2W5ZTVQKIBQH3ebYzafth1WTy+qlHoBY4I8kVFUhYJyHXLKqqXh2B3nCjCpj+0bBWsQvFBajfOFlegc4m23/c/8fAL7zhdBda8CE6/gjUf9OWAXy6pRYzDBXaW8zE80dGloTcm3H1hMZhG3fLQL/aP88cykHjbzrVqTIF4De2ZrtVr4+fmhrKwMvr6+Tj23wWDAmt82QB/eFxP7RCDU1x0/H8nG0z8dtzmuS6g3UvIr0KedL35//Hrc/cU+7D9fjJem9EJxpR4fbU3BPcOi8dq0WCz67RSW7Um3+XklTBgQ6oac/HyEuhsg6CoQ4y1i/phIrN6XjPziErhDD3cYcHvfQHQNVFt22zXpAKMOR8/nIadEi0hvJcoqqqCCEW6CCRoY0SPMAzU11Sgqq4SXCgjzViK/tAJuMNX7MsMNRiiFv/yvA7koEYK0C4CgECDUC0/1/zWaAb1JhAhAqVTAXerhE2rzT93xlQYzqg1my7khIMTHvfada90xeeU6GMwi/DxUKK0yAgoF/L3ckVdugAmWIGuGgDA/D5TrRJTVGOHvqUZxlQFmKBAV6IkwX48G7bQUcklobOzf2uMOppegrNqIIR0DUVCuR2phFcTa+wUAogI9UV5jQnGVHr0i/RHkrYGfpwYiBGxIzEON0VJnXOdg+Lir8EdiHkQACoUCtw9sj5yyaoT6ekDjppRue0dyITKKqtA/OgAnL2hhMJkR7ueBnLIaDI4JhKe+GB06dgQUShSU61BQoUdMoBd8PVQorNSjoFyH7ecKEerrDqVCwIXSGozvFW4Zmm20bsvjk5xfgV+PX5RqiwnyxPRBUdh0Og/Hs8swKCYQx7LKYDSLeOSGzgisHwjrncf2MgHrT+bgaGapFP7NEODnoUZJtRHeGiWem9QDAFBtNOO9+HNQCAKeGt8d7irL79DRzBL8eDgb/moRPl6eyCqpxp1DovDjoWyYIKBfe38czy7FDd1DER3gge/2ZwAABkQF4J5hMTZt+2Z/BhJq2zJ9cFS9XsNLfjdq/zWYRfxztSUEX981BH8fFt3gd6Tp/1vOu3R7Mg6ll0p/T69Ni0W7AA/b+wpASbURn+9IxaAOgRjfyxKMiitqsG7nMbgFRuKPxDzpHFP6RmDmdR0att36ffvBWHciH0+tToC/pwq7n73xsm8imqs5r98MLHYYDAZs2LABN910E1QqSxdGSn4Fxr27o9Hjl9weixlDo/HfXefxyvozGNElCKE+7lh77AKendQDj43uLF13qbljuuDjSz5p2UfjBp3RDL3JjBFdgrAnpQgPjeyI52/pZXPcrK8PYse5Ajx+Yxd8tNX2HEM6BKBbmA9WHMjEI6M64d839cR1r21pdAKdADOUMGPT3Otw9uA2TBh3I1RKBWA2AWYjIJow66v9yCzUwlcjoFpnwPQB4Xh4ZAxgNgOiyXKsaMbh9CK8u/kMOgZ6YEiMH9Ydy4ICIpQw495h7bHyQCYUMGNCzxBU1OhxJK0QQzsGYFSXILz/ZxL8NALGdAvBxsSLmNw7DMM7BSKzqBzL96RBgAhPlQJ6gwHRgR6YObQ9skuqsOpABvzdFYj0c0dKfhkGtvfDyM4BgGjGqYul2JdSALUSMJtMECCinZ8G43qEIKe0EjuS8uGhUkAhGqE3iVDAjJgADwyM9gNEs6UuiJYZe6JouUw0oaRKB3elAHc3ASezi1FRbUCglwpmsxkVNXooal9O/T2UKK82INhLBbXSsluvj0YBtVJAeVVNbX+E5dgAT+vPG+CpEhDio0ZOSRVEiAj2UkOjtAw9lVbWwGg2QwHAW6OERinAYDKjUmeAovblUCkAnmolBNFcuzZfhCiK0BtNtY+55RiIZgZWIrJhevo8xn9+EucLKjF/YnfMGXP53tHmas7rN4eErkCnYC/4uLuhvMaIyX3C8UeiZbWMj7sbbusfCQAY2zMMr6w/gwPni9El1NJV2D7AMhYe5lvXRfmPGzrhp8PZ6B7mg96RdQ9WsLcGHYM9pR06fd3dMG1Ae+xJKcJ/d6fhYHoxPv37ILTz94DRVLcBW6+IunN0DfXGhdJqHEovwfHa1TOx7SzdwME+auRqa6AQYLOZXPtAL2QVV+N4ng5KpQfgEQCo6saaagwm7CnyhlH0wquT++A/axPx5nEBY0Z3leq0OpKRir1mJYLaRWLAjd3w1JHt0nUzug/GqHAdMoorccuE7jiaWYrnP9+H+Gw3eA7pg59MEegX5I/n7x6BQfW6PXV55fh6504AwLhuYfjzTB7aGTww8/obsX1/Bj42JWJM+xDcMywG7357GIEFaux/aCzUbgr8d3UC1hov4MHrOmL53nSYzCJeGt4LGNERvjoj3n9nhxTiVEoBBpMI9xIFDjw2Dn4eKpvazuZqEeHngT9O5uC5NSfhrXHDhN5hWKO9AC+1EvGP3oD1J3Lw6oa6YPrChF5Y/Ptp9AryhcFkRrK+Am/e2hfaaoMUYCP83JFTVoMOPp64UG6Z+Pq/B+MQGhOIV1ccxfqTOfCBGz6/dxCyS6rxzP9OSOeP9vLE1n/dgNf/OIuvdqfh5tgIHEgrQmGFHq9NjsU9w6KlY9cdy8Y/Vx+X6rTyU4koM6A27ABKwRJu3p7eFz8cyEBC7dCEZTBOlAKWNWypFMBdQ9rjhwMZGN45CAdSLUMSg2MC8Nm9g/DWpiT8eCgTY3uGIK5TIIZ1CEC4r0bagPHZ/53AnpQCTOoVBk+1AusSLtT2fwARfhosmzUYGjcFIIowmkyY9ske6I0mBHiqoK3SSe8z3VUC5k/ojt+OX8DJ7FLEtvPFK7f1xvNrT+JMThmUMGNEpwAcyyyCyWTpZ3nr9t5IK6rClzuSbcKjABEPjIhBe38PRPi5S0GwqEKHpduSUViuwz1D2+O6joFSIIRouV6ACB93JfakFGJjYg4EiPCrHQ5xUwD/vqkHVh/KQnJeOf42MBJnc8tx+mIZBKC270eEj0ZApc4k3c+9I3zgoVLUfv6VeElbLXN1RncPRodATyzfmwaFADw6qiO2ns3HubxyCLWPrQARI8PMiIhsjzXHLkApiPBQu6FSZ0Dd+2vLeXuE+6BTsCc2JeYAAAI9VSip0qNXhA+GdgiAttqA345fAEQRfdr5orhSj5zSKnQP80FSXjkUCgFT+7fD2qPZECBiav8InMwuRUZRJXpF+KBXhHV4SoQI0dL7ZiWKqNQZcEJaLSiia6gXUvMttXQM8kRG7Uq28T3DoFIKOJWjRWZRJQQAGjcBEEWYzWaIggCzWURnXxFqLz+czdFCIQCiaIa3WonoIA+czSlHoJcaZlG02TG8X3s/BHqpUVBeA42bgITMUum+VCsFeKiVcFMI6BHuA6Vg2UU7raAcZVWW3wMPldJmZVO4jwZFFTp0DvVCpc6InNIqBHlpEB3oIZ03t6waudoaqBQCuoZ542ztPC0fjeVxCvN1R4i3ynrXAQBMZjOS88tr/6QsvxNRAR7ILq2W/s4EiAj0UqO4Ug8BgLe7G/zc3eCpVtbbDNXy768ncnC+oBJ+HircFxcDOTGwXAGFQsAdA9vj9xMX8dzkHkgvqsKZHC3+Nqg9PNWWu7RjsBc6h3ghtaBSWrpnDSzDOgbCz0OFkV2C8dykHpg3vhtUCoXNGPydg9tj7o1dMHvZIRxMK8aA6ACM6xmKfu39cDy7DCeyy/Dhn8noFemLJX+cQY3BXHsbnvBSK1GpN+GFW3ohJb8CL/9+WhpPlgJL7Rhkvyh/5GstE06DvdW4sXsovtmXgXf/TIGvqMAJRRLuHhqDLqHe2HQqFwXlOhhru9fvGRqNLWfysfVsPl78JRHfPDAUH29NwdHMEjw9obs07t45xAsxQV4YHBOAw7UT7kJ93HFjjzCp3sExAWjn74ELpdX4vrYbtn3typ36Y7T15xIM6xiIP8/kIVdbA5NZxOnaP+Zu4T4Y0z0EoT4a5JfrEH86Dzd0D5E2ibuhWwjKawzYk1KEm/tGAAC8NG5Y9ch1mPHlfuSU1eDBER2wLakQSXnlGPLKn+gX5YdPZg6CSingxV9O4dfjF+Hj7gZd7f1eoTNizVHLHhev3R6LSH8PXN8tGNhgaauHSolb+kbgnc1JUjvVbgpM6hOOI/VWZf3fmC54YV0i0mtX5ozoEoRBMZZVXa9O64OCch0Ophfjke+OIKR2iffjN3bB9/szkFlchT8Sc6U5ETfFRmBQTABe/v00PtySjNsHtpPuS+tKsLuGRFn2HinXYWKvUAzVXMRrx1XSpOp+Uf44llmK03k1OJJdBSNs53JE+rnjYlkNNG4KRPp7IK2wEsuOalENX/Tu2glbMk2oMZjhGRgBeIdiaKyApYe0WHXGgFVn8uCpLsQzE7tj1vAOyCquxo8pgCiG4O6JIxHm6459JYcAWCYq7y3V4f7fy2Ayi5h7YxeE+Ghw0tAO3ho3DOoTabuxnh64+/caAEFwV4Xgw7tHQQj2wsI5g5BZXAUvtRvC/dzxz9UJWHvsAlSCCP/YCQgs1WHLtp3SadoHeCC7pBqbdll//1R48ZZeuL5rCKYu3YMLZZa/p3PpPvjjtuuliZRf7U7D4vWnAVj+1rw0Mcgw1U52rJ1CMKJjENzjrsN914lIL6pChyBPrPj5BH7KsqxSGtklGEcySlBdZXmRiw70RGZxFXoYfeAuKJFgKpV+xy1/Jwpc3zUE8afz8F6i5c3LaeMg9Gvvh8fHj0SSIhnvXjhn8/glKU1o59EFnxrTMKl3OF6+rTemf74PGUVV6BbmjXO1Kwsf79YFIyd0R9L/TmDVoSyg9qnKr1CFg/8YiwU/Hsd6gyXMqLItIVghAL/ePhIvLt0Do1GEMbIPnj+YiE4hXrj7b6NxYncaFv9+GqElGnx16xDEtvfDi78k4ucj2fjvrMHoEe6LnecKMKJLMO796gDOFlmeR4d2DMTTE7rj8c/3AQCe6d8dS7emoFJvwuPBXXBjj1DM//kEUgwVEARAvGQRTIi3Gv/uWoVOA0fi4U/2S5e/OLEXqkO88MCyQ+iu8UFmcRWqDSYM6xiIA2nFuEEVgh5BPvg88bz0Rq9ziBeySqqhrzEDtR3WY8NDoTeasevSVWuXzgmunXPfqcYL+VodKvRGQA/M6hGD5yb3hNFsxoTXt0Jbu89UdLknMvVV6BTshVv6ReLDLcmY0SkKS27vC8Ayofe9P8+hoFyHXbpC9Aj3QedQb6w/kQM/rQplOts7Yt3DIzB16R6pbW4VAt65sx9u698OgGWz0vf/PIePfrP02D98fUf4uNu+cWttLRZYli5dirfeegu5ubno168fPvroIwwdOrTJ43/66Se88MILSE9PR9euXfHGG2/gpptuaqnmXbWFt/bGS1N6QRAEvDSlF348nIW5l3SVje8VjtQdqQAsE/56R1qe3EJ93XHk+XFQKgQIgmAZcwbQIcgLajcFDCYz7h4SDU+1G5bNHoIfD2dhdPdQ+Huq8cvckTiYVow7P9+HtQkX8Ovxi1JYAYAgbzVW/yMOhRU6jOoWghFdgvHL8Ys4nlUKH3c3xNROlgurnYg2pEMgLpRW40JpNXpG+CKucxC+2ZdRO0lOgdN7MvDjkQuY2r+dNJ4LAD0jfKTad6cUYm9qEa5/Y5vUQ1H/j3VA7YSu2wa0qxdYbCdtKRQCpvSLxGc7UqVeJWvAq69+T8eAaH+ph2D2soPSbfaJ9IObUoHpg9tj6bZULN2WgmV70pCrrUGglxqDYgKkVTj1dQj2wk+PDMVH/9uGuaM7oVu4L+b9eBx6kxmH0ktw9xf7UFZtkPalsK7uGNczFBdKa3AmR4sZQ6OlP/juYT4I8dGgoFyHCD93hPm645/jukm9Ljd2D4Wvu0rqmRIE4Na+kUjOK8fxrFJc1zkIj91QtwLL31ONbx8cinu/OoBD6SWo0BnhoVLioZGdoFQIeP/PZLy1KQmZxVUQBEvYcVcp8d9d53GxrAZP/HAMS2cOhEqpkFZqjOwSjCdu7IoagxkRvips2HAR3cO8cTrH8uJwfZdgHMssxZqj2TCaRYT7uqO0Wo8agxnB3mo8MqoTFv52GjfHRqBjsBfeiT8nvYvsGe6L6EBPnMurQJif5fdtWMdA6UU2zFeDPK0OC387jaS8chRX6iGKwKhuIegSalm+/tOjwwEAGxNz8ej3R7C3NnSmrj6OUd0scwb6tvez6Z188299cSyzFJtP5aKoUo//3NwLHWo/EkOlVNhMjrxnWDTWJVxAtLfld7BjsJf0O6VUCPj4noFY/PtpaKsNuFBajdIqAxasOYlhnYJwobQaHYO9kFtWg7O55fhqdxryy3XoEe6DN/44W3t7AgordCissDy+gZ5qaafnkV1Cah93y+1e6tlJPZBWVIknfjgGwBIu39qUhLO55bDOUb17SBQ+rB0CHt8rHB/e3R9f7jqPJX+clYKxNfB2Cqm7jdHdQ7A9qQBHixQ4XmGZz3BzX8scvY1PjkJBuQ56k1ka+ra+6f73zT2RkFUKsyiipMqAgnId3vgjCetP5EAQLKHKugz+0Rs6o087P0T4uyOruFratG5IbXvuGNgOqw5mIjm/AtM/34u7h0Tj232W55gnfjgGd5US2SXV0uMR7K3GioeuQ/dwH5vtEQI81fD3VKNSX42Ptqbg853noTeaIQjA/Ind8ebGJEzuE44wX3cs35uOKX0joBBT0TXUG2o3BfRGMwI8Vbh7aJS05N26PYKPxg0v3NILt3+6FzvOFUifbWbtlb6uUxBCCiqw/3wxAjxVKK8xYsvZupV9A6L9cV9cDP65um7Oo/VNp5X1NoO91Sis0OObfRn4/UQOOod4Q1tjhLtKgRqDWdpU9PlbekpLkU9f1GLzqVz4eqjw9E/HbbaDePj6ThgQ7Y8NJ3Ok4/sEmJFYokCIjwZ92/lhVLcQJOVq0THYC/vPF+PJVQnYl1qEh0d1wifbUqUduGcMjbrsatDW0iKBZfXq1Zg3bx4+++wzDBs2DO+//z4mTpyIpKQkhIaGNjh+7969mDFjBpYsWYJbbrkFK1euxNSpU3H06FH06dOnJZroFNZ3U9d1CsJ1jSwrnDOmMzzVSgyMDsCILkE2y9jclA2Xh3lp3PDf+wbDLIrSLHwvjRvuH9HR5rghHQLQP8pf2p/D+i7Qz0OFYG8NIv3rXuiVCgFv3BGLv//3AG6qt5Tuwes7QqkU8MCIjjiWWYL1J3IwoXc4JvYOx/cPDkNReTX2HD6Gs/oAnMjW2oQVwLK8GwBigrzw8YwB+OfqBGmIaWjHQOw/XwwfjRvmTeiGG2rDwS2xEVj822mE+GgQ1Mgs83uGRmPFgQwpCDQWWNRuCsQEeSJPW4OuoT6ICvTE+YJK7EouhCAAdw+Jxk2xll6TOwdH4dPtqdITt4/GDcvvHyLtddGYMF93DA8ToVEpcfvA9hjbIwwZxZV4YPlhaXlsl1BvvPm3vrhYWo1zueV4pDZUHMsswfDOdct1BUHA9V2DseboBUT4W16wZ4/ogLXHLuB0jhZ3DmkPwDLZ8t839YCvuwp+niq8fFvTv/PuKiU+uHsAbvpwF0qrDJg2sB38PFWYFdc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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves(record_dict, sample_step=500):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    # plot\n",
    "    for idx, item in enumerate(train_df.columns):\n",
    "        plt.plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        plt.plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        plt.grid()\n",
    "        plt.legend()\n",
    "        # plt.xticks(range(0, train_df.index[-1], 10*sample_step), range(0, train_df.index[-1], 10*sample_step))\n",
    "        plt.xlabel(\"step\")\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "plot_learning_curves(record, sample_step=500)  #横坐标是 steps"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.2955\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "loss = evaluating(model, val_loader, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 拿到中间输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "logits, deep_output = model(train_ds[:][0].to(device), return_deep_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([11610, 30])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deep_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "# 从这看到deep部分抽取到的特征分布，有些特征没那么重要，或许可以消减一些神经元\n",
    "plt.imshow(deep_output.cpu().detach().numpy().mean(axis=0).reshape(1, -1))\n",
    "plt.yticks([])\n",
    "plt.show()"
   ]
  }
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